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An NVIDIA Developer article describes how NVIDIA FLARE can be used to run and scale federated learning workloads across common orchestration environments — Docker, Kubernetes, and Slurm — offering guidance for moving beyond single-server, few-client setups to larger deployments.
This arXiv poster reports empirical results for FedV-KGQA, a federated approach to multi-hop question answering over vertically partitioned knowledge graphs where organizations share entity identifiers but keep disjoint relation types. Each silo trains local KG embeddings and a server concatenates silo-specific entity views, anchors the question at a topic entity, and ranks candidates without exchanging raw triples or relation embeddings; experiments show federated fusion recovers most centralized accuracy, anchoring and enrichment matter more than embedding choice, and the cheapest encoder depends on target accuracy; the paper also provides four design lessons and an interactive prototype with released checkpoints.
Google Research authors propose a "social learning" framework in which teacher LLMs transfer knowledge to a student LLM using natural-language instructions and synthesized examples rather than sharing raw data. Evaluated with PaLM 2‑S on tasks including spam detection, grade‑school math, and text-based question answering, they find that generated examples (e.g., 8–16 per case) often preserve privacy while yielding comparable student performance for most tasks, though spam detection was a notable exception; the paper also proposes quantitative privacy measures for this setting.
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